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- W848137551 abstract "Accurate registration of dynamic contrast-enhanced (DCE) MR breast images is challenging due to the temporal variations of image intensity and the non-rigidity of breast motion. The former can cause the well-known tumor shrinking/expanding problem in registration process while the latter complicates the task by requiring an estimation of non-rigid deformation. In this paper, we treat the intensity's temporal variations as corruptions which spatially distribute in a sparse pattern and model them with a L1 norm and a Lorentzian norm. We show that these new image similarity measurements can characterize the non-Gaussian property of the difference between the pre-contrast and post-contrast images and help to resolve the shrinking/expanding problem by forgiving significant image variations. Furthermore, we propose an iteratively re-weighted least squares based method and a linear programming based technique for optimizing the objective functions obtained using these two novel norms. We show that these optimization techniques outperform the traditional gradient-descent approach. Experimental results with sequential DCE-MR images from 28 patients show the superior performances of our algorithms." @default.
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- W848137551 date "2015-12-01" @default.
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- W848137551 title "Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images" @default.
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- W848137551 doi "https://doi.org/10.1016/j.compmedimag.2015.05.004" @default.
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